Abstract
The continuing development of avionics for Unmanned Aircraft Systems (UASs) is introducing higher levels of intelligence and autonomy both in the flight vehicle and in the ground mission control, allowing new promising operational concepts to emerge. One-to-Many (OTM) UAS operations is one such concept and its implementation will require significant advances in several areas, particularly in the field of Human–Machine Interfaces and Interactions (HMI2). Measuring cognitive load during OTM operations, in particular Mental Workload (MWL), is desirable as it can relieve some of the negative effects of increased automation by providing the ability to dynamically optimize avionics HMI2 to achieve an optimal sharing of tasks between the autonomous flight vehicles and the human operator. The novel Cognitive Human Machine System (CHMS) proposed in this paper is a Cyber-Physical Human (CPH) system that exploits the recent technological developments of affordable physiological sensors. This system focuses on physiological sensing and Artificial Intelligence (AI) techniques that can support a dynamic adaptation of the HMI2 in response to the operators’ cognitive state (including MWL), external/environmental conditions and mission success criteria. However, significant research gaps still exist, one of which relates to a universally valid method for determining MWL that can be applied to UAS operational scenarios. As such, in this paper we present results from a study on measuring MWL on five participants in an OTM UAS wildfire detection scenario, using Electroencephalogram (EEG) and eye tracking measurements. These physiological data are compared with a subjective measure and a task index collected from mission-specific data, which serves as an objective task performance measure. The results show statistically significant differences for all measures including the subjective, performance and physiological measures performed on the various mission phases. Additionally, a good correlation is found between the two physiological measurements and the task index. Fusing the physiological data and correlating with the task index gave the highest correlation coefficient (CC = 0.726 ± 0.14) across all participants. This demonstrates how fusing different physiological measurements can provide a more accurate representation of the operators’ MWL, whilst also allowing for increased integrity and reliability of the system.
Highlights
Advancements in technologies such as Artificial Intelligence (AI), sensor networks and agent-based systems are rapidly changing the operations of Unmanned Aircraft Systems (UASs) and are introducing systems with higher levels of intelligence and autonomy [1]
In this paper we present a study with two physiological sensors, including an EEG and eye tracker, as well as a secondary task performance index and a subjective questionnaire as measurements of Mental Workload (MWL) in an OTM UAS wildfire detection mission
The performance measures included the average task index value and controller input count across each phase, while the physiological measures included the average value of EEG index and visual entropy in each phase
Summary
Advancements in technologies such as Artificial Intelligence (AI), sensor networks and agent-based systems are rapidly changing the operations of Unmanned Aircraft Systems (UASs) and are introducing systems with higher levels of intelligence and autonomy [1]. A negative effect of this complexity is the human operators’ loss of Situational Awareness (SA) and the increase in Mental Workload (MWL). The measurement of cognitive load, MWL, in real-time gives CPS the ability to sense and adapt to the human operator. The system allows dynamic adaptation of the system Automation Level (AL) and actual command/control interfaces, while maintaining desired MWL and the highest possible level of situational awareness. This new adaptive form of HMI2 is central to support the airworthiness certification and widespread operational deployment of One-to-Many (OTM) systems in the civil aviation context [6,7,8]. The operators’ resulting MWL can be an outcome of the task demand and endogenous factors such as experience, effort, stress and fatigue [25]
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